Data fusion for catheter tracking using Kalman filtering: applications in robot-assisted catheter insertion
Bibliographic record
Abstract
X-ray image guided angioplasty is a minimally invasive procedure that involves the insertion of a catheter into a blood vessel to remove blockages to blood flow. There are several issues associated with conventional angioplasty which cause risks for the patient (damage to blood vessels, dislodging plaques, etc.) and difficulties for the clinician (X-ray exposure, fatigue, etc.). Autonomous or semi-autonomous robot-assisted catheter insertion is a solution that can reduce these problems substantially. To perform autonomous catheter insertion, closed-loop position control of the distal tip of the catheter is required during insertion. Therefore accurate real-time position feedback is needed for this purpose. We have developed a real-time image processing algorithm for catheter tip position tracking which has an acceptable performance but is sensitive to X-ray image artifacts caused by bones and dense tissues. A magnetic tracking system (MTS) is another modality that has also been used for catheter tip position tracking, but it is sensitive to external electromagnetic interferences and ferromagnetic material. Combining the measurement data provided by both imaging and magnetic sensors can compensate for the deficiencies of each and can also improve the robustness of catheter tip position tracking. We have developed a Kalman filter based sensor fusion scheme to overcome deficiencies of both of these methods and create a reliable real-time tracking of a catheter tip. Experiments have been performed by inserting a guide catheter into a model of the vasculature. The method has been tested in presence of occlusion in the images and also electromagnetic interference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".